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首页> 外文期刊>Journal of Hydroinformatics >ANFIS-based approach for scour depth prediction at piers in non-uniform sediments
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ANFIS-based approach for scour depth prediction at piers in non-uniform sediments

机译：基于ANFIS的非均匀沉积物码头冲刷深度预测方法

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摘要

An estimation of scour depth is a prerequisite for the efficient foundation design of important hydraulic structures such as bridge piers and abutments. Most of the scour depth prediction formulae available in the literature have been developed based on the analysis of the laboratory/field data using statistical methods such as the regression method (RM). Conventional statistical analysis is generally replaced in many fields of engineering by the alternative approach of artificial neural networks (ANN) and adaptive network-based fuzzy inference systems (ANFIS). These recent techniques have been reported to provide better solutions in cases where the available data is incomplete or ambiguous by nature. An attempt has been made to compare the performance of ANFIS over RM and ANN in modeling the depth of bridge pier scour in non-uniform sediments. It has been found that the ANFIS performed best amongst all these methods.
机译：冲刷深度的估算是有效设计重要水工结构（如桥墩和桥台）的前提。文献中可用的大多数冲刷深度预测公式都是基于使用统计方法（例如回归方法（RM））对实验室/现场数据进行分析得出的。在许多工程领域中，常规统计分析通常被人工神经网络（ANN）和基于自适应网络的模糊推理系统（ANFIS）的替代方法取代。据报道，这些最新技术可在可用数据本质上不完整或模棱两可的情况下提供更好的解决方案。试图比较ANFIS和RM和ANN在模拟非均匀沉积物中桥墩冲刷深度时的性能。已经发现，在所有这些方法中，ANFIS表现最佳。

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